EpiExploreR: a Shiny web application for the exploration and analysis of animal disease data
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Experimental Zooprophylactic Institute of Abruzzo and Molise G. Caporale, Italy
Emerging and re-emerging infectious diseases are a significant public and animal health threat and their early detection and immediate response are crucial for their control. The detection of an outbreak or an increase of cases of zoonotic disease (e.g., West Nile Fever, Brucellosis) in animals, could be the first signal, for public health authorities, to start the implementation of prevention programs.
Unfortunately, emerging infectious diseases, especially vector-borne diseases, are more challenging to be predicted and controlled and require a strong multidisciplinary approach due to the interaction among host, vectors, pathogen, and environment. Their understanding needs the analysis of complex and diverse data, often disconnected from each other and coming from different sources [Pollett et al., 2017]. Especially the environmental component involves the use of data which, although widely available nowadays, are unstructured and extremely large. All this makes such data of limited use if not converted through proper data management and analytical methods that can handle the heterogeneous datasets, transforming these into useful information for decision and policy makers [Pfeiffer et al. 2015].
In recent years, the availability of software packages, the increased computational capability, and the wider accessibility to data have facilitated the analyses of public and animal health surveillance data. However, their full development is still difficult for many users because advanced programming skills are often required. To overcome these lack in technical skills, desktop and web applications have been developed, providing analysis tools, ready to use for researchers, and public and animal health professionals (e.g. EpiContactTrace [Nöremark M & Widgren S, 2014], SpatialEpiApp [Moraga P, 2017] and EpiSignalDetection tool [ECDC, 2018]).
However, despite the growing effort, most of the recent integrated applications still lack managing simultaneously different needed datasets and making at the same time analytical tool available for a complete epidemiological assessment.
In this context, we developed EpiExploreR, a user-friendly, flexible R-Shiny web application. EpiExploreR dashboard provides different tools integrating several common approaches used to analyze spatial and spatio-temporal data on animal diseases in Italy, including notified outbreaks, surveillance of vectors, animal movements or contacts data, and remote sensed data coming from different sources.
It is organized in main topics that can be summarized as follow:
• accessing and exploring different sources of geo-referenced, nearly real-time data, including notified outbreaks, surveillance of vectors, animal movements, and remote sensed data;
• applying base methods for early outbreak detection (e.g. Farrington algorithm, spatio-temporal cluster analysis, and data correlation tools);
• running and calibrating temperature driven mosquito models;
• performing network analysis useful in the identification of disease transmission patterns.
The dashboard displays analysis results at one glance by interactive maps, graphs, and table (Fig.1) following a "what you see is what you analyze" scheme.
Two versions of EpiExploreR shiny applications are web distributed using ShinyProxyIO. One, letting the user to access restricted ministry data, requires authentication, and the other, freely accessible, let the user to upload self-owned data in excel format.
EpiExploreR can be used in several ways to answer a lot of epidemiological issues. We present the main features of the application through three case studies:
1. Velocity estimate of BTV1 spreading in Central Italy during 2014. Although sophisticated approaches may be used to estimate disease velocity [Nicolas et al. 2018], the simpler observed space-time ratio using the first date of outbreak occurrence can be a useful insight into the formulation of hypotheses in the disease spread investigation. Fig. 2 shows the estimate of the velocity of spread of the BTV-1 across Central Italy during 2014. The results show estimated velocity (space/time) along each segment of the on map drawn line ranging from 0.48 km/day to 2.28 km/day corresponding to 3.3 km/week and 15.96 km/week while the overall measured velocity is of 1.23 km/day (about 8.6 km/week).
2. West Nile Disease (WND) in the Sardinia region. Cases of WND in Sardinia are reported since 2011, however, it’s considered endemic since 2014 [Cappai et al. 2017]. In 2018 an increase of WND was observed in comparison with the previous four years. The epidemic curve indicates that the number of outbreaks ranged from one to three per week in the disease favorable season until 2017, whilst during 2018 it reached fourteen outbreaks per week. A preliminary analysis of temperatures was performed to verify abnormalities in 2018 possibly explaining the increased number of cases, and it was observed that the daily mean temperatures (°C) recorded in May and June 2018 were on average about 4 degrees lower than the overall average of the five years investigated. Temperature-driven mosquito modeling applied to estimate the abundance of mosquitoes in Sardinia in the 2014-2018 period shows a high correlation with the number of outbreaks of West Nile Disease recorded for the same period. In particular, the results obtained show the correlation between adult mosquitoes simulated and WND outbreaks falling inside the region on a monthly basis confirming the hypothesis of favorable temperature conditions for the abundance of mosquitoes during the year 2018 (Fig.3).
3. Alive clusters detection for Brucellosis and evaluation of disease introduction at risk areas through animal movements network analysis in Italy. Using the SatScan tool for the identification of Brucellosis alive clusters in Italy on April 2019 two significant clusters were detected in northern of Sardinia (p-value <0.05) with 29 outbreaks during March and April 2019. Network analysis tools applied to bovine and sheep movement data in the period 01/01/19 - 30/03/19 preceding the time of clusters detection and related to the identified area shows that the majority of movements were registered towards the north of Italy and then targeted surveillance measures should be applied (Fig 4).
The application is addressed to scientists, researchers, including policy makers and public and animal health professionals wishing to test hypotheses and explore data on disease surveillance activities without any advanced statistical or programming skill.
Legend:
Fig.1. EpiExploreR dashboard features scheme
Fig.2. Estimated velocity of the BTV-1 spreading in Central Italy in 2014 by use of EpiVelocity task. A grid layer with a user-defined spatial resolution of 0.4 decimal degree and colored on the basis of the minimum occurrence time of the set of outbreaks falling in each pixel of the grid is added on the map. The estimated velocity (space/time) along each segment of the on map drawn line ranges from 0.48 km/day to 2.28 km/day corresponding to 3.3 km/week and 15.96 km/week while the overall measured velocity is of 1.23 km/day (about 8.6 km/week) as shown in the table.
Fig.3. Correlation (as scatter-plot and double axis time series) between simulated Adults and WND outbreaks falling inside the (pink) region drawn on the map on a monthly basis for 2014-2018 years.
Fig.4. Panel A shows an example of the trace forward subnetwork linking a node in Sardinia to the north of Italy. The trace forward network representation add details about the timing of the edges, the number of animals moved and the type of farms involved (coded through markers color and icons as detailed in the legend). Panel B shows all the reached nodes in the valid temporal paths from the Sardinia areas to the rest of Italy (red color of the density of the nodes) from 01/01/2019 to 30/03/2019.
References
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• Pfeiffer DU, Stevens KB. Spatial and temporal epidemiological analysis in the Big Data era. Prev Vet Med. 2015;122: 213–220. doi:10.1016/j.prevetmed.2015.05.012
• Nöremark M, Widgren S. EpiContactTrace: an R-package for contact tracing during livestock disease outbreaks and for risk-based surveillance. BMC Vet Res. 2014;10: 71.
• Moraga P. SpatialEpiApp: A Shiny web application for the analysis of spatial and spatio-temporal disease data. Spat Spatio-Temporal Epidemiol. 2017;23: 47–57. doi:10.1016/j.sste.2017.08.001
• ECDC. EpiSignalDetection tool. In: European Centre for Disease Prevention and Control [Internet]. 17 Dec 2018 [cited 31 Dec 2018]. Available: http://ecdc.europa.eu/en/publications-data/episignaldetection-tool
• Nicolas G, Tisseuil C, Conte A, Allepuz A, Pioz M, Lancelot R, et al. Environmental heterogeneity and variations in the velocity of bluetongue virus spread in six European epidemics. Prev Vet Med. 2018;149: 1–9. doi:10.1016/j.prevetmed.2017.11.005.
• Cappai S, Rolesu S, Coccollone AM, Loi F, Meloni G, Foxi C, et al. Reoccurrence of West Nile Virus Disease in Humans and Successive Entomological Investigation in Sardinia, Italy, 2017. J Anim Sci Res. 2017;2. doi:10.16966/2576-6457.108.
Keywords:
Spatio-temporal analyses,
Network analysis,
Livestock mobility,
Zoonoses,
VECTOR BORNE DISEASES,
R-Shiny,
web application
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Regular oral presentation
Topic:
Operational GIS tools for policy-makers, planners, researchers
Citation:
Savini
L,
Conte
A,
Perticara
S,
Morelli
D and
Candeloro
L
(2019). EpiExploreR: a Shiny web application for the exploration and analysis of animal disease data.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00053
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Received:
10 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Lara Savini, Experimental Zooprophylactic Institute of Abruzzo and Molise G. Caporale, Teramo, Abruzzo, Italy, l.savini@izs.it
Dr. Luca Candeloro, Experimental Zooprophylactic Institute of Abruzzo and Molise G. Caporale, Teramo, Abruzzo, Italy, l.candeloro@izs.it